pursueorigin / OptML-SVRG-PyTorch

Implementation of SVRG for training neural networks

SVRG for neural networks (PyTorch)

Implementation of stochastic variance reduction gradient descent (SVRG) for optimizing non-convex neural network functions in PyTorch, according to [1].

This is a joint work with Yusheng Hu and Bryant Wang during the Optimization for Machine Learning (Fall 2019) course at Columbia University.

[1] Zeyuan Allen-Zhu and Elad Hazan, Variance Reduction for Faster Non-Convex Optimization, ICML, 2016

Code

git clone https://github.com/yueqiw/OptML-SVRG-NonConvex.git
# python 3.6

Train neural networks with SVRG

python run_svrg.py --optimizer SVRG --nn_model CIFAR10_convnet --dataset CIFAR10 --lr 0.01
python run_svrg.py --optimizer SVRG --nn_model MNIST_one_layer --dataset MNIST --lr 0.01
python run_svrg.py --optimizer SGD --nn_model MNIST_one_layer --dataset MNIST --lr 0.01

Run experiments to compare SVRG vs. SGD

python run.py --CIFAR10_SVRG_lr_search
python run.py --CIFAR10_SGD_lr_search
python run.py --SVRG_small_batch_lr_search
python run.py --SGD_small_batch_lr_search

python run.py --CIFAR10_SVRG_lr_search
python run.py --CIFAR10_SGD_lr_search
python run.py --CIFAR10_SVRG_small_batch_lr_search
python run.py --CIFAR10_SGD_small_batch_lr_search

Plot the training curve results

python plot_mnist_results.py 
python plot_cifar_results.py